bims-meluca Biomed News
on Metabolism of non-small cell lung carcinoma
Issue of 2020‒07‒26
three papers selected by
Cristina Muñoz Pinedo
L’Institut d’Investigació Biomèdica de Bellvitge

  1. Mol Ther Oncolytics. 2020 Sep 25. 18 189-201
    Kuo TC, Huang KY, Yang SC, Wu S, Chung WC, Chang YL, Hong TM, Wang SP, Chen HY, Hsiao TH, Yang PC.
      Targeting metabolic reprogramming is an emerging strategy in cancer therapy. However, clinical attempts to target metabolic reprogramming have been proved to be challenging, with metabolic heterogeneity of cancer being one of many reasons that causes treatment failure. Here, we stratified non-small cell lung cancer (NSCLC) cells, mainly lung adenocarcinoma, based on their metabolic phenotypes and demonstrated that the aerobic glycolysis-preference NSCLC cell subtype was resistant to the OXPHOS-targeting inhibitors. We identified that monocarboxylate transporter 4 (MCT4), a lactate transporter, was highly expressed in the aerobic glycolysis-preference subtype with function supporting the proliferation of these cells. Glucose could induce the expression of MCT4 in these cells through a ΔNp63α and Sp1-dependent pathway. Next, we showed that knockdown of MCT4 increased intracellular lactate concentration and induced a reactive oxygen species (ROS)-dependent cellular apoptosis in the aerobic glycolysis-preference NSCLC cell subtype. By scanning a panel of monoclonal antibodies with MCT4 neutralizing activity, we further identified a MCT4 immunoglobulin M (IgM) monoclonal antibody showing capable anti-proliferation efficacy on the aerobic glycolysis-preference NSCLC cell subtype. Our findings indicate that the metabolic heterogeneity is a critical factor for NSCLC therapy and manipulating the expression or function of MCT4 can be an effective strategy in targeting the aerobic glycolysis-preference NSCLC cell subtype.
    Keywords:  MCT4; MCT4-neutralizing antibody; NSCLC; Sp1; aerobic glycolysis; cancer metabolism; metabolic heterogeneity; ΔNp63α
  2. J Proteome Res. 2020 Jul 22.
    You L, Fan Y, Liu X, Shao S, Guo L, Noreldeen HAA, Li Z, Ouyang Y, Li E, Pan X, Liu T, Tian X, Li X, Ye F, Xu G.
      Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. 339 metabolites were identified in metabolic profiling. And 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues, amino acids, purine metabolites, fatty acids, phospholipids and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in LCa. Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.
  3. Cancers (Basel). 2020 Jul 16. pii: E1926. [Epub ahead of print]12(7):
    Hao D, Sengupta A, Ding K, Ubeydullah ER, Krishnaiah S, Leighl NB, Shepherd FA, Seymour L, Weljie A.
      The metabolic requirements of metastatic non-small cell lung (mNSCLC) tumors from patients receiving first-line platinum-doublet chemotherapy are hypothesized to imprint a blood signature suitable for survival prediction. Pre-treatment samples prospectively collected at baseline from a randomized phase III trial were assayed using nuclear magnetic resonance (NMR) spectroscopy (n = 341) and ultra-high performance liquid chromatography - mass spectrometry (UPLC-MS) (n = 297). Distributions of time to event outcomes were estimated by Kaplan-Meier analysis, and baseline characteristics adjusted Cox regression modeling was used to correlate markers' levels to time to event outcomes. Sixteen polar metabolites were significantly correlated with overall survival (OS) by univariate analysis (p < 0.025). Formate, 2-hydroxybutyrate, glycine and myo-inositol were selected for a multivariate model. The median OS was 6.6 months in the high-risk group compared to 11.4 months in the low risk group HR (Hazard Ratio) = 1.99, 95% C.I. (Confidence Interval) 1.45-2.68; p < 0.0001). Modeling of lipids by class (sphingolipids, acylcarnitines and lysophosphatidylcholines) revealed a median OS = 5.7 months vs. 11. 9 months for the high vs. low risk group. (HR: 2.23, 95% C.I. 1.55-3.20; p < 0.0001). These results demonstrate that metabolic profiles from pre-treatment samples may be useful to stratify clinical outcomes for mNSCLC patients receiving chemotherapy. Genomic and longitudinal measurements pre- and post-treatment may yield addition information to personalize treatment decisions further.
    Keywords:  NMR; UPLC-MS; lipidomics; metabolomics; non-small-cell-lung cancer; overall survival